algorithms and software. Faster and more
effective algorithms and software for nonlinear, mixed-integer, and linear
infeasibility in optimization. Ways of
reaching a feasible solution more quickly for nonlinear and mixed-integer programs,
and of analyzing infeasible optimization models. Spin-off
applications from algorithms for analyzing infeasibility.
formulation assistants. Automated tools
for analyzing and debugging optimization models. For example, one
tool analyzes the shape of nonlinear functions and regions to help select
the correct solver.
optimization. Examples include transistor
sizing, DSP task-to-processor assignment, flexible manufacturing systems, forestry,
scheduling, task assignment in cloud computing, channel assignment in
wireless networks, 3G communications optimization.
Data classifiers. A new approach for finding good data classifiers arises
from an infeasibility analysis algorithm. What is the best way to
use this to develop better data classifiers?